\(\diamond \)  Motivation
Nowadays, researchers seek a way to distinguish between cover images and stego images focusing on a specific steganographic scheme in a fraction of a second. However, the problem occurs when we do not know the scheme; which makes steganalysis challenging, especially in real-time processing. To solve this problem, scientists utilize a universal steganlaysis which enables them to recognize if a given image is a stego or cover. This universal steganalysis has to detect all steganographic schemes to become able to classify images by extracting important features. The more features we extract, the higher will be the chance of detecting the steganographic schemes that lead us to classify images accurately. However, the high number of features generates a high dimensional problem known as the curse of dimensionality (CoD). This is an important problem to solve because analyzing the features in details requires a lot of time and is not easy to do in real-time. Furthermore, prior works [1]} [2]} have found the CoD problem to be an NP-hard problem and have attempted to solve it by reducing the dimensionality of the data.
